Data visualization is a critical component of data science, but it can be challenging to master. Luckily, there are certain techniques that can help data scientists improve the quality and impact of their data visuals. These must-know data visualization techniques will help you get started on top-notch projects.
1. Quick Data Visualization
In situations where data visualization is needed promptly, creating visuals directly through Python is the best route. It can be done quicker than setting up colorful, artistic visualizations through specialized software, using a few extra lines of code. This is often the first method among data visualization techniques that data scientists use to create visuals. While it may only create basic visualizations, this is often all that’s needed.
Packages can easily be imported to Python to enable the creation of a variety of graph types, as well, from simple bar graphs to hexbin plots. Many Python libraries specialize in specific niches of visualization, offering different tools to meet different needs. Matplotlilb, for example, is popular due to its position as the oldest visualization library, with other popular libraries, like Pandas, built on top of it. The graphs produced through Python code using open-source libraries like this are typically just fine for meetings with other data scientists or technical colleagues.
2. Presenting Data for Non-Technical Colleagues
One of the key benefits of data visualization is that it makes otherwise complex data easy to understand. In fact, it has even been proven to influence decision-making in business environments.
High-quality visualization is valuable for the clarity of everyone involved in a project, but it is especially helpful for working with those who are not as technically inclined as other data scientists. People in this category might be stakeholders or marketing executives. These audiences will be better able to understand and appreciate data sets when they are presented in a visually appealing, engaging way. The best technique for accomplishing this is by using data visualization software, which will add extra polish, clarity, and aesthetic appeal to data set visuals.
There are several great data visualization software tools on the market, but Tableau is a popular choice among data scientists. Tableau is flexible and approachable, with many visually appealing graph, chart, and map options available. Along with Tableau, Microsoft Excel remains a top choice for data visualization. Since most people already have this program installed on their computers, Excel is a great option for those who don’t want to pay for or download more software. While Excel may be challenging for some to pick up at first, data scientists should catch onto Excel’s language fairly quickly, allowing them to create highly appealing visuals in a familiar platform.
3. Showing Unique Data Sets
Some kinds of data may benefit from unique data visualization techniques and approaches in place of a more standard chart or graph. It can be tempting to go with basic scatter plots or pie charts no matter the data set being represented.
The standard set of graphs, plots, and charts is familiar to everyone. However, more niche data sets can be better served by finding visualization methods specific to that variety of data. For geographic data, for example, using a map or a hexbin plot might be more helpful than representing regions’ data on a bar graph. Another example is heat data, which could be shown on a scatter plot, but might be more impactful on a hexbin or heatmap instead.
4. Presenting Complex Data Sets
The main idea of data visualization is to make complicated data easier to comprehend and therefore easier to apply and utilize. This can be more challenging for data that is inherently complex, though, even when communicating with other data scientists. Representing complex data requires its own unique approach, especially if the data visuals are for non-technical colleagues.
A good place to start is by deciding whether the data can be coherently represented in one visualization or if breaking it down into separate graphs and illustrations would work better. It isn’t always possible to separate complex data.
For example, a data set might be complicated because it shows a high volume of data with a few interconnected aspects. Representing those aspects individually might take away from the meaning of the data visualization, rendering it less effective than one large, well-executed visual. It may also help to consider what the goal of the visualization is. Is it conceptual or data-driven? Exploratory or declarative?
5. Creating Accessible Data Visualization
When creating data visualizations, it is important to consider everyone who may need to understand the visuals, including those with visual limitations. Examples include people with forms of color blindness or partial blindness. These people are important team members as well and should be able to benefit from visualizations just as much as their colleagues. Data scientists who are able to create accessible visualizations will come across as more perceptive and professional, as well.
Methods for making visualizations more accessible to those with visual impairments vary. There are different forms of color blindness that vary in severity, but certain techniques can be helpful across the board.
One key tip for making color-blind-friendly visualizations is to avoid using red and green components together. Red-green colorblindness is the most common type, according to the National Eye Institute. Colorblind people do see color, they simply see it differently, so certain colors may be virtually indistinguishable to them. To counteract this, try using a colorblind-friendly color palette that is designed to be easier to see clearly. It will work just as well for non-colorblind people, too.
In addition to an accessible color palette, using a large, bold, clear font is always a good idea. It looks professional in general, but it will also make it easier for people with partial blindness or poor vision to read the text notations on the visuals.
Creating Better Data Visualizations
Data visualization is an integral part of data science. It allows the impact of data sets to be understood and experienced by everyone involved with a project. Visuals can be highly influential and compelling, but remembering to communicate in a way that is clear and accessible is crucial, no matter the data type. Using these data visualization techniques and tips will get you started on the path to creating data visualizations that are polished, effective, and engaging for all.
By Alex Landa, ODSC